Extracting Regulatory Signals from the Upstream
Region of Co-expressed Genes Derived from DNA
Microarray Experiments

Shuichi Kawashimashuichi@kuicr.kyoto-u.ac.jp

Hiroyuki Ogata

ogata@kuicr.kyoto-u.ac.jp

Minoru Kanehisa

kanehisa@kuicr.kyoto-u.ac.jp

Institute for Chemical Research, Kyoto University
Gokasho, Uji, Kyoto 611-0011, Japan

1 Introduction
Since the various genome sequencing projects are rapidly producing new sequence data, it is now
possible to analyze information of sequence data at the genomic scale. In addition, a large amount ofdata for gene expression pro les produced by the DNA microarray technology will be available in near
future. Since the co-expression of genes strongly indicates co-regulation by complex genetic networks,
the data of families of co-expressed genes derived from these pro les are useful for understanding
the mechanisms of genetic networks in living organisms. Thus computational method for analyzing
regulatory sequences of co-expressed genes is required.
Here, we present an analysis of upstream regions of the sets of genes grouped according to a large
scale expression pro le experiment.

2 Data and Methodvan Helden et al. developed a method that isolates DNA binding sites for transcription factors fromfamilies of co-regulated genes [2]. The method is based on a regulatory paradigm in yeast that the upstream regions of the genes contain multiple copies of regulatory sequences. We employed the method
to detect regulatory sites for the groups of genes determined by a microarray experiment on Saccharomyces cerevisiae [1]. The method counts the number of occurrences of all oligonucleotides of theselected size (six in the present analysis), and estimates their statistical signi cance. The probabilityof observing exactly n occurrences of oligonucleotide b is estimated according to the binomial formula:Pr(occfbg = n j F fbg) =!
2 (F fbg)n 2 (1 0 F fbg)T 0n(T 0 n)! 2 n!
T

where T is the total number of possible matching position, and F fbg is the frequency of b observedin T . To estimate the signi cance of the observed occurrence n against background probability, weemployed the signi cance index given by Tamames et al [3].
sig= logPr(occfbg = n j Fob fbg)
Pr(occfbg = n j Fbg fbg)

where Fbg fbg is the background frequency of b observed in all the upstream sequences (800 bases
upstream of the initiation codon) in the genome, and Fob fbg is the frequency of b observed in theupstream sequences in the group of genes.

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3 Result and Discussion
We analyzed three groups of genes reported in DNA microarray experiments [1]. In Table 1, oligonucleotides occurring in the upstream region of one group are shown with their signi cance indices. Thisgroup include seven genes, namely GYS2, CTT1, HSP42,HSP26, HSP12, YKL026 and CYGR043C,
which are known to have stress response elements (STRE; AGGGG or CCCCT) in their non-coding
upstream regions. Interestingly, oligonucleotide patterns that match to STRE sequences have highly
signi cant indices as shown in the Table 1. Also for the second group, which consists of MLS1,
IDP2, ICL1, ACS1, ACR1, FBP1 and PPC1, oligonucleotide patterns that partially match to theknown regulatory element (CCRTYCRTCCG) are detected with highly signi cant indices. For the lastgroup, which consists of YPL012W, YNL141W, YMR290C, SAM1, GPP1, YGR160W and YDR398W,
oligonucleotide, TTTTTT, observed extremely high signi cance ( sig = 31:80 ). Although the function of this pattern, as long as we know, is not characterized, the sequences may have relation to theregulation of this group.
Sequence Occurrence signi cance Sequence Occurrence signi cancetggctc84.64
tagggg42.25
cagggg53.54
cgacgt42.20
ataggg63.22
ccctaa52.20
cctgga6
3.09
ggggga4
2.18
ggctct6
2.88
ctggct5
2.14
ggggcg4
2.86
agggga5
2.10
gggggg4
2.64
aggggc4
1.93
atgggg5
2.54
ggcggc4
1.90
taaaac10
2.51
tgccag5
1.87
aggggg4
2.31
gcgacc3
1.78
Table1. oligonucleotides with high signi cance.
In this study, we have shown the e ectiveness of the method to characterize regulatory sequencesof a set of genes de ned by expression pro le data. We plan to detect other functionally related genesthat are co-regulated with the groups we analyzed. Correlation analysis of expression patterns could
be used to enlarge the grouping of genes.

Acknowledgements
This work was supported in part by a Grant-in-Aid for Scienti c Research on Priority Areas `Genome
Science' from The Ministry of Education, Science, Sports and Culture in Japan. The computation time
was provided by the Supercomputer Laboratory, Institute for Chemical Research, Kyoto University.

References[1] DeRisi, J.L., Iyer, V.R. and Brown, P.O., Exploring the metabolic and genetic control of gene
expression on a genomic scale, Science, 278:680{686, 1997.
[2] van Helden, J., Andre, B. and Collado-Vides, J., Extractiong regulatory sites from the upstream
region of yeast genes by computational analysis of oligonucleotide frequencies, J. Mol. Biol.,
281:827{842, 1998.
[3] Tamames, J., Casari, G., Ouzounis, C. and Valennncia, A., Conserved clusters of functionallyrelated genes in two bacterial genomes, J. Mol. Evol., 44:66{73, 1997.
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